Skip navigation
Skip navigation

Regularization in kernel learning

Mendelson, Shahar; Neeman, Joseph


Under mild assumptions on the kernel, we obtain the best known error rates in a regularized learning scenario taking place in the corresponding reproducing kernel Hilbert space (RKHS). The main novelty in the analysis is a proof that one can use a regularization term that grows significantly slower than the standard quadratic growth in the RKHS norm.

CollectionsANU Research Publications
Date published: 2010
Type: Journal article
Source: The Annals of Statistics
DOI: 10.1214/09-AOS728


File Description SizeFormat Image
01_Mendelson_Regularization_in_kernel_2010.pdfPublished Version344.57 kBAdobe PDFThumbnail

Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  20 July 2017/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator